Random Forest (RF)
is a supervised machine learning technique. The `forest' it builds is an ensemble of many decision trees, usually trained with the `bagging' method. The underlying principle behind the ensemble model is that a group of weak learners come together to form a strong learner. Ensemble learners thus improve the performance of single classifiers by inducing several classifiers and combining them to obtain a new classifier that outperforms every one of them [1]}. RF is scalable to any number of dimensions and usually has acceptable performance. However, it adds additional randomness to the model while growing the trees. For example, instead of searching for the most important feature while splitting nodes, it searches for the best feature among a random subset of features. RF thus prevents the overfitting of datasets by creating random subsets of the features.
